CN111475266A - Diversity-constrained crowd sensing task allocation method - Google Patents

Diversity-constrained crowd sensing task allocation method Download PDF

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CN111475266A
CN111475266A CN202010149178.8A CN202010149178A CN111475266A CN 111475266 A CN111475266 A CN 111475266A CN 202010149178 A CN202010149178 A CN 202010149178A CN 111475266 A CN111475266 A CN 111475266A
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task
users
tasks
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CN111475266B (en
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张立臣
丁宇
宋星辰
李黎
郭龙江
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Shaanxi Normal University
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
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Abstract

A diversity-constrained crowd sensing task allocation method comprises the steps of collecting users and sensing tasks, determining available users of the tasks, selecting task allocation users, and adjusting the users allocated by the selected tasks. The invention considers the timeliness of the perception task and the discontinuity of the idle time of the mobile user, increases the available user part for determining the task in the task allocation process, selects the user meeting the task requirement to allocate the task only from the users including the task execution time period in the idle time period, reduces the communication and calculation pressure of the server platform, improves the speed and quality of task allocation, simultaneously solves the technical problem caused by the perception task being unable to be completed in the effective time period, and expands the application range of the group intelligence perception. The method has the advantages of high task allocation quality, wide application range and the like, and can be applied to crowd sensing task allocation.

Description

Diversity-constrained crowd sensing task allocation method
Technical Field
The invention belongs to the technical field of crowd sensing, and particularly relates to a crowd sensing task allocation method based on diversity constraint.
Background
With the rapid development of embedded devices, wireless sensor networks, internet of things, intelligent mobile terminals and the like, pervasive intelligent systems integrating sensing, computing and communication capabilities are being widely deployed and gradually merged into the daily living environment of people, and the capability of pervasive computing for acquiring data is greatly enhanced. In this context, urban and social awareness are leading research hotspots in the current information field. The city perception task has the characteristics of wide range, large scale, heavy task and the like. The existing urban sensing system mainly depends on pre-installed professional sensing facilities such as a camera and an air detection device, has the problems of limited coverage range, high investment and maintenance cost and the like, and is limited in application range, objects and application effect. Therefore, a new perception mode, namely mobile crowd sensing, is created. Different from the traditional perception technology depending on professionals and equipment, the mobile crowd sensing turns the attention to a large number of common users, and the large number of common users are used as sensing sources, and intelligent mobile terminals carried by the users, such as smart phones, wearable equipment and the like, are utilized to form a large-scale perception system which is closely related to the daily life of people at any time and any place. Crowd-sourcing awareness emphasizes awareness by leveraging the broad distribution, flexible mobility, and opportunistic connectivity of the masses and provides intelligent assistance support for city and social management. The method can be applied to a plurality of important fields, such as intelligent transportation, public safety, socialized recommendation, environment monitoring, urban public management and the like.
A crowd-sourcing aware application system typically comprises 3 components: server platform, data consumer and data provider. A data user issues a perception task demand to a server platform and receives a task result from the server platform; the server platform is mainly responsible for task issuing, task allocation, perception data collection and task quality evaluation; and the data provider, namely a common mobile user carrying the mobile intelligent equipment, receives the tasks distributed by the server platform, is responsible for data sensing and collection, and sends the sensing data to the server platform. In the crowd sensing system, task allocation is the basis for implementing crowd sensing. The server platform needs to allocate each allocable task to a plurality of users meeting task constraint conditions according to task requirements submitted by data users and the state of the current online user, and the allocation is based on realizing specific optimization targets, such as maximizing task receiving rate, maximizing overall profit and the like.
The current task allocation method and system facing the crowd sensing has not considered the following aspects:
1. the timeliness of the task and the discontinuity of the idle time of the mobile user are perceived.
2. The diversity requirements of each perception task on the user types possessed by the user population providing the perception data.
In the existing task allocation method, it is generally considered that a data user does not have a completion time constraint on a submitted perception task, and a mobile user who completes the perception task is a user who has no type difference and can execute the task at any time. In fact, since the sensing tasks submitted by the data users are completed in different time periods, the generated data have great differences in properties, and the differences directly affect the completion quality of the tasks, the sensing tasks often have clear limits on the execution time of the tasks, the mobile users who complete the sensing tasks often have different types, the requirements of each task on the type diversity of the assigned users are different, and the users whose user type diversity distribution meets the task requirements cooperate to complete the tasks to obtain higher-quality sensing data. Therefore, the execution time requirement and the user type diversity requirement of the perception tasks and the collection of the idle time period and the user types of the users can be increased in the task allocation process, and different perception tasks can adjust the users meeting the task execution time requirement according to the respective user type diversity requirements, so that the benefit of task completion is maximized. However, the current online task allocation method does not take these factors into consideration, and the application range of crowd sensing is limited to a great extent.
Therefore, in order to meet the crowd sensing application scenario meeting the actual requirements and improve the task allocation quality and the task allocation profit of the platform, it is necessary to design a crowd sensing task allocation method with diversity constraints under the condition of considering the execution time requirement of the sensing task, the type diversity requirement of the user selected by the sensing task and the task allocation profit maximization requirement.
Disclosure of Invention
The technical problem to be solved by the present invention is to overcome the above disadvantages of the prior art, and to provide a crowd sensing task allocation method with diversity constraints, which has high task allocation quality and wide application range.
The technical scheme adopted for solving the technical problems comprises the following steps:
(1) collecting user and perceptual tasks
Set of perceptual tasks ═ τ1，τ2，...，τnComposed of n tasks, where each task τiIs (N)i,si,ei) Corresponding to an execution time interval(s)i,ei) And a desired number of users Ni，siIs task τiStart time of (e)iIs task τiEnd time of (d) satisfies ei>siI ∈ {1, 2.·, n }, n being a finite positive integer, k being a finite set of user types C ═ C1,c2,…,ckThe number of the types of the users contained in the data.
User set U ═ U1,u2,…,umIs composed of m users, where each user ujCorresponding to a user type cj∈ C, an idle time interval(s)j,ej) And a set of executive tasks τiYield v ofijWherein, j ∈ {1, 2.. multidot.m }, e }j>sj，vij≥0。
Each task τiFor each user type ct∈ C has diversity constraint parameters
Figure BDA0002401829200000031
And
Figure BDA0002401829200000032
wherein, t ∈ {1, 2.., k },
Figure BDA0002401829200000033
(2) determining available users for a task
Determining per task τi∈ available user set AiThe following were used:
Ai＝{uj|uj∈U,sj≤si,ej≥ei}
(3) selecting task assignment users
1) For each task τi∈, each user type ct∈ C, by user ujRevenue v for executing tasksijThe available user sets A are respectively set from large to smalliThe user type is ctUser set of
Figure BDA0002401829200000034
Wherein:
Figure BDA0002401829200000035
2) for each task τi∈, each user type ct∈ C, respectively from the available user set A as followsiThe user type is ctUser set of
Figure BDA0002401829200000036
Before selection in
Figure BDA0002401829200000037
Task tau is formed by individual usersiAssignable user set Ui：
Figure BDA0002401829200000038
Figure BDA0002401829200000041
Figure BDA0002401829200000042
Wherein
Figure BDA0002401829200000043
Is a set of available users AiThe user type is ctUser set of
Figure BDA0002401829200000044
The number of the users in (1) is,
Figure BDA0002401829200000045
is a set of assignable users UiThe user type is ctIs selected.
3) Selecting a task to be assigned tau according to the following formulapTo be assigned task τpMoving into task set of allocated users'
Figure BDA0002401829200000046
|UiI is an allocable user set UiThe number of the users in (1) is,
Figure BDA0002401829200000047
is a set of assignable users UiThe user type is ctUser set of
Figure BDA0002401829200000048
If there is no task T to be allocated satisfying the conditionpThen the allocation ends.
4) For each user type ct∈ C Press user ujExecution of task to be assigned τpYield v ofpjThe tasks to be allocated are respectively treated from large to smallpAssignable user set UpThe user type is ctUser set of
Figure BDA0002401829200000049
InLine ordering, wherein
Figure BDA00024018292000000410
5) For each user type ct∈ C, respectively, from the task to be assigned τ according to the following formulapAssignable user set UpThe user type is ctUser set of
Figure BDA00024018292000000411
In, before selection
Figure BDA00024018292000000412
A user joins a task to be allocated taupSelected user set Up’
Figure BDA00024018292000000413
Figure BDA00024018292000000414
Figure BDA00024018292000000415
Wherein N ispIs the task τ to be assignedpThe number of persons required for the purpose of the treatment,
Figure BDA00024018292000000416
is the task τ to be assignedpSelected user set Up’The user type is ctIs selected.
6) Per user ujExecution of task to be assigned τpYield v ofpjFrom large to small pairs of sets Up-Up’The users in (1) are ranked and the top N is selectedp-|Up’I users join selected user set Up’Wherein | Up’Is the selected user set Up’The number of users in (1).
(4) Adjusting users assigned to selected tasks
1) Finding out the user u with the minimum profit in the selected user setmin∈Up’And the user u with the maximum profit in the unselected user setmax∈Up-Up’If there is no user u with the minimum profitminAnd the user u with the maximum profitmaxSatisfies the following formula, step 5 of step (4) is carried out
vp,min＜vp,max
Wherein v isp,minAnd vp,maxRespectively the least profitable user uminAnd the user u with the maximum profitmaxExecution of task τpThe gain of (1).
2) Selecting the demoble user u 'with the minimum profit'min
Figure BDA0002401829200000051
Wherein v ispjIs user ujExecution of task to be assigned τpThe yield of (a) to (b) is,
Figure BDA0002401829200000052
is the task τ to be assignedpFor user ujUser type cjThe inverse diversity constraint parameter of (2) is,
Figure BDA0002401829200000053
is the selected user set Up’The user type is cjUser set of
Figure BDA0002401829200000054
The number of users in (1); if no go-to user u 'with minimum income exists'minGo to step 5 of step (4).
3) Selecting addable user u 'with maximum income'max
Figure BDA0002401829200000055
Wherein,
Figure BDA0002401829200000056
is the task τ to be assignedpFor user ujUser type cjForward diversity constraint parameters; if no addable user u 'with maximum profit exists'maxGo to step 5 of step (4).
4) Adding user u 'with maximum income'maxMove into Up’Go-to user u 'with minimal revenue'minMove out Up’
Up'＝Up'∪{u'max}-{u'min}
Go to step 1 of step (4).
5) Updating and task to be distributed tau according to the following formulapTask tau with time conflictsconf∈confCorresponding available user set Aconf
Aconf＝Aconf-Up'
Wherein
Figure BDA0002401829200000061
Wherein(s)conf,econf) Is related to task to be assigned τpTask tau with time conflictsconfAnd (4) turning to the step (3) until no task to be allocated meeting the condition exists.
In the step (1) of collecting users and perception tasks, the number N of the required users of the perception task setiIs any positive integer, NiIs integral multiple of k, and k is the number of types contained in the limited user type set C.
In the step (1) of collecting users and perceiving tasks, the number k of the user types contained in the limited user type set C is optimally 3.
In the step (1) of collecting users and perceiving tasks, the diversity constraint parameters
Figure BDA0002401829200000062
Figure BDA0002401829200000063
In step 3) of step (3) of selecting task allocation user, the task to be allocated is taupComprises the following steps: the number of tasks satisfying the condition is at least 1, and the task t to be distributedpThe task corresponding to the assignable user set with the minimum number of users.
According to the invention, the diversity constraint condition of each task to each user type is considered in the task allocation process, and the users allocated to the selected tasks are adjusted on the premise of meeting the constraint condition, so that the benefit of completing the perception task and the stability and reliability of perception data are improved. In addition, the method for classifying the users through the user positions or the user reliability in the prior art can serve the generation of the user types in the invention, and compared with the method for classifying the users according to a certain characteristic of the users, the user type and user type diversity constraint in the invention has higher generality and universal applicability.
The invention considers the timeliness of the perception task and the discontinuity of the idle time of the mobile user, increases the available user part for determining the task in the task allocation process, selects the user meeting the task requirement to allocate the task only from the users including the task execution time period in the idle time period, reduces the communication and calculation pressure of the server platform, improves the speed and quality of task allocation, solves the problem caused by the perception task not being completed in the effective time period, and expands the application range of the group intelligence perception.
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FIG. 1 is a flowchart of example 1 of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, but the present invention is not limited to the examples described below.
Example 1
The diversity-constrained crowd-sourcing aware task allocation method of the embodiment comprises the following steps:
(1) collecting user and perceptual tasks
Set of perceptual tasks ═ τ1，τ2，...，τnComposed of n tasks, where each task τiCorresponding to an execution time interval(s)i,ei) And a desired number of users Ni，siIs task τiStart time of (e)iIs task τiEnd time of (d) satisfies ei>siI ∈ {1, 2.·, n }, n being a finite positive integer, k being a finite set of user types C ═ C1,c2,…,ckThe number of the types of the users contained in the data. Sensing number of required users of task set NiIs any positive integer, NiIs integer multiple of k, k is the number of types contained in the limited user type set C, task tauiCan be expressed as (N)i,si,ei) In this embodiment, N is 4, k is 3, N1Is 3, N2Is 6, N3Is 9, N4Is 6, τ1Is (3,0,2), τ2Is (6,1,3), τ3Is (9,2,4), τ4Is (6,4,6), C is { C1,c2,c3}。
User set U ═ U1,u2,…,umIs composed of m users, where each user ujCorresponding to a user type cj∈ C, an idle time interval(s)j,ej) And a set of executive tasks τiYield v ofijWherein, j ∈ {1, 2.. multidot.m }, e }j>sj，vijNot less than 0, m in this example is 20, u1User type c1Is c2,u2User type c2Is c3,u3User type c3Is c2,u4User type c4Is c3,u5User type c5Is c1,u6User type c6Is c2,u7User type c7Is c2,u8User type c8Is c3,u9User type c9Is c2,u10User type c10Is c1,u11User type c11Is c2,u12User type c12Is c1,u13User type c13Is c2,u14User type c14Is c3,u15User type c15Is c2,u16User type c16Is c3,u17User type c17Is c1,u18User type c18Is c2,u19User type c19Is c1,u20User type c20Is c3。
u1Is (0,4), u2Is (0,3), u3Has an idle time interval of (2,6), u4Has an idle time interval of (4,6), u5Has an idle time interval of (4,6), u6Has an idle time interval of (4,6), u7Has an idle time interval of (1,4), u8Has an idle time interval of (1,4), u9Has an idle time interval of (1,4), u10Is (0,3), u11Is (0,3), u12Has an idle time interval of (1,3), u13Has an idle time interval of (4,6), u14Is (2,4), u15Has an idle time interval of (2,6), u16Has an idle time interval of (1,3), u17Is (0,2), u18Has an idle time interval of (2,6), u19Has an idle time interval of (4,6), u20Is (1, 3). Benefit v of the present embodimentijThe values are as follows:
Figure BDA0002401829200000081
each task τiFor each user type ct∈ C has diversity constraint parameters
Figure BDA0002401829200000082
And
Figure BDA0002401829200000083
wherein, t ∈ {1, 2.., k },of the present embodiment
Figure BDA0002401829200000092
And
Figure BDA0002401829200000093
the values are as follows:
Figure BDA0002401829200000094
Figure BDA0002401829200000095
(2) determining available users for a task
Determining per task τi∈ available user set AiThe following were used:
Ai＝{uj|uj∈U,sj≤si,ej≥ei}
in the step of the method,
A1＝{u1,u2,u10,u11,u17}，
A2＝{u1,u2,u7,u8,u9,u10,u11,u12,u16,u20}，
A3＝{u1,u3,u7,u8,u9,u14,u15,u18}，
A4＝{u3,u4,u5,u6,u13,u15,u18,u19}。
(3) selecting task assignment users
1) For each task τi∈, each user type ct∈ C, by user ujRevenue v for executing tasksijThe available user sets A are respectively set from large to smalliThe user type is ctUser set of
Figure BDA0002401829200000096
Wherein:
Figure BDA0002401829200000097
Figure BDA0002401829200000098
is AiOf type CtThe set of users of (a), in this step,
Figure BDA0002401829200000099
Figure BDA00024018292000000910
Figure BDA0002401829200000101
Figure BDA0002401829200000102
Figure BDA0002401829200000103
Figure BDA0002401829200000104
Figure BDA0002401829200000105
Figure BDA0002401829200000106
Figure BDA0002401829200000107
Figure BDA0002401829200000108
Figure BDA0002401829200000109
Figure BDA00024018292000001010
2) for each task τi∈, each user type ct∈ C, respectively from the available user set A as followsiThe user type is ctUser set of
Figure BDA00024018292000001011
Before selection in
Figure BDA00024018292000001012
Task tau is formed by individual usersiAssignable user set Ui
Figure BDA00024018292000001013
Figure BDA00024018292000001014
Figure BDA00024018292000001015
Wherein
Figure BDA00024018292000001016
Is a set of available users AiThe user type is ctUser set of
Figure BDA00024018292000001017
The number of the users in (1) is,
Figure BDA00024018292000001018
is a set of assignable users UiThe user type is ctIs selected. In the step of the method,
U1＝{u17,u1,u2}，
U2＝{u12,u10,u9,u11,u1,u20,u16,u8}，
U3＝{u3,u1,u7,u15,u8,u14}，
U4＝{u5,u19,u6,u3,u15,u4}。
3) selecting a task to be assigned tau according to the following formulapTo be assigned task τpMoving into task set of allocated users'
Figure BDA0002401829200000111
|UiI is an allocable user set UiThe number of the users in (1) is,
Figure BDA0002401829200000112
is a set of assignable users UiThe user type is ctUser set of
Figure BDA0002401829200000113
If there is no task T to be allocated satisfying the conditionpThen the allocation ends. In the step, the number of tasks meeting the condition to be distributed existsWith an order of 1, task τ to be assignedpIs the task corresponding to the distributable user set with the minimum number of users, and the task to be distributed is tau2。
4) For each user type ct∈ C Press user ujExecution of task to be assigned τpYield v ofpjThe tasks to be allocated are respectively treated from large to smallpAssignable user set UpThe user type is ctUser set of
Figure BDA00024018292000001111
Is ranked by the user in (1), wherein
Figure BDA0002401829200000114
UpIs task τpCan be assigned to a set of users,
Figure BDA0002401829200000115
is UpOf type CtIn this step, p is 2,
Figure BDA0002401829200000116
Figure BDA0002401829200000117
Figure BDA0002401829200000118
5) for each user type ct∈ C, respectively, from the task to be assigned τ according to the following formulapAssignable user set UpThe user type is ctUser set of
Figure BDA0002401829200000119
In, before selection
Figure BDA00024018292000001110
A user joins a task to be allocated taupSelected user set Up’
Figure BDA0002401829200000121
Figure BDA0002401829200000122
Figure BDA0002401829200000123
Wherein N ispIs the task τ to be assignedpThe number of persons required for the purpose of the treatment,
Figure BDA0002401829200000124
is the task τ to be assignedpSelected user set Up’The user type is ctIs selected. In the step of the method,
Figure BDA0002401829200000125
Figure BDA0002401829200000126
Figure BDA0002401829200000127
U2'＝{u12,u10,u9,u11,u20,u16}。
6) per user ujExecution of task to be assigned τpYield v ofpjFrom large to small pairs of sets Up-Up’The users in (1) are ranked and the top N is selectedp-|Up’I users join selected user set Up’Wherein | Up’Is the selected user set Up’The number of users in (1).
(4) Adjusting users assigned to selected tasks
1) Finding out the user u with the minimum profit in the selected user setmin∈Up’And the user u with the maximum profit in the unselected user setmax∈Up-Up’If there is no user u with the minimum profitminAnd the user u with the maximum profitmaxSatisfies the following formula, step 5 of step (4) is carried out
vp,min＜vp,max
Wherein v isp,minAnd vp,maxRespectively the least profitable user uminAnd the user u with the maximum profitmaxExecution of task τpIn which step user uminAnd user umaxMin is 10 and max is 8.
2) Selecting the demoble user u 'with the minimum profit'min
Figure BDA0002401829200000131
Wherein v ispjIs user ujExecution of task to be assigned τpThe yield of (a) to (b) is,
Figure BDA0002401829200000137
is the task τ to be assignedpFor user ujUser type cjThe inverse diversity constraint parameter of (2) is,
Figure BDA0002401829200000132
is the selected user set Up’The user type is cjUser set of
Figure BDA0002401829200000133
The number of users in (1); if no go-to user u 'with minimum income exists'minGo to step 5) of step (4), where the lowest profitability can go to user u'minIs present of u'minIs u10。
3) Selecting addable user u 'with maximum income'max
Figure BDA0002401829200000134
Wherein,
Figure BDA0002401829200000135
is the task τ to be assignedpFor user ujUser type cjForward diversity constraint parameters; if no addable user u 'with maximum profit exists'maxTurning to step 5) of step (4), wherein the earning rate is maximum, and the user u 'can be added'maxIs present of u'minIs u8。
4) Adding user u 'with maximum income'maxMove into Up’Go-to user u 'with minimal revenue'minMove out Up’
Up'＝Up'∪{u'max}-{u'min}
Go to step 1 of step (4).
5) Updating and task to be distributed tau according to the following formulapTask tau with time conflictsconf∈confCorresponding available user set Aconf
Aconf＝Aconf-Up'
Wherein
Figure BDA0002401829200000136
Wherein(s)conf,econf) Is related to task to be assigned τpTask tau with time conflictsconfUntil there is no task to be allocated which satisfies the condition, the execution time interval of (2) is transferred to step (3),confis { tau1,τ3Is updated
A1＝{u1,u2,u10,u17}，
A3＝{u1,u3,u7,u14,u15,u18}，
After the task allocation is finished, the task allocation result is obtained as
U1'＝{u17,u1,u2}，
U2'＝{u12,u9,u11,u20,u16,u8}，
U4'＝{u5,u19,u6,u3,u4,u15}。
And completing diversity-constrained crowd sensing task allocation.
Example 2
The diversity-constrained crowd-sourcing aware task allocation method of the embodiment comprises the following steps:
(1) collecting user and perceptual tasks
Set of perceptual tasks ═ τ1，τ2，...，τnComposed of n tasks, where each task τiCorresponding to an execution time interval(s)i,ei) And a desired number of users Ni，siIs task τiStart time of (e)iIs task τiEnd time of (d) satisfies ei>siI ∈ {1, 2.·, n }, n being a finite positive integer, k being a finite set of user types C ═ C1,c2,…,ckThe number of the types of the users contained in the data. Sensing number of required users of task set NiIs any positive integer, NiIs integer multiple of k, k is the number of types contained in the limited user type set C, task tauiCan be expressed as (N)i,si,ei) In this embodiment, N is 2, k is 3, N1Is 4, N2Is 5, τ1Is (4,0,2), τ2Is (5,2,4), C is { C1,c2,c3}。
User set U ═ U1,u2,…,umIs composed of m users, where each user ujCorresponding to a user type cj∈ C, an idle time interval(s)j,ej) And a set of executive tasks τiYield v ofijWherein, j ∈ {1, 2.. multidot.m }, e }j>sj，vijNot less than 0, m in this example is 10, u1User type c1Is c3,u2User type c2Is c1,u3User type c3Is c2,u4User type c4Is c3,u5User type c5Is c2,u6User type c6Is c1,u7User type c7Is c2,u8User type c8Is c1,u9User type c9Is c2,u10User type c10Is c1。
u1Is (0,4), u2Is (2,4), u3Is (0,2), u4Is (0,2), u5Is (0,2), u6Is (2,4), u7Is (2,4), u8Is (0,2), u9Is (0,4), u10Is (0, 2). Benefit v of the present embodimentijThe values are as follows:
Figure BDA0002401829200000151
each task τiFor each user type ct∈ C has diversity constraint parameters
Figure BDA0002401829200000152
And
Figure BDA0002401829200000153
wherein, t ∈ {1, 2.., k },
Figure BDA0002401829200000154
of the present embodiment
Figure BDA0002401829200000155
And
Figure BDA0002401829200000156
the values are as follows:
Figure BDA0002401829200000157
Figure BDA0002401829200000158
(2) determining available users for a task
Determining per task τi∈ available user set AiThe following were used:
Ai＝{uj|uj∈U,sj≤si,ej≥ei}
in the step of the method,
A1＝{u1,u3,u4,u5,u8,u9,u10}，
A2＝{u1,u2,u6,u7,u9}。
(3) selecting task assignment users
1) For each task τi∈, each user type ct∈ C, by user ujRevenue v for executing tasksijThe available user sets A are respectively set from large to smalliThe user type is ctUser set of
Figure BDA0002401829200000159
Wherein:
Figure BDA00024018292000001510
Figure BDA00024018292000001511
is AiOf type CtThe set of users of (a), in this step,
Figure BDA00024018292000001512
Figure BDA00024018292000001513
Figure BDA00024018292000001514
Figure BDA0002401829200000161
Figure BDA0002401829200000162
Figure BDA0002401829200000163
2) for each task τi∈, each user type ct∈ C, respectively from the available user set A as followsiThe user type is ctUser set of
Figure BDA0002401829200000164
Before selection in
Figure BDA0002401829200000165
Task tau is formed by individual usersiAssignable user set Ui
Figure BDA0002401829200000166
Figure BDA0002401829200000167
Figure BDA0002401829200000168
Wherein
Figure BDA0002401829200000169
Is a set of available users AiThe user type is ctUser set of
Figure BDA00024018292000001610
The number of the users in (1) is,
Figure BDA00024018292000001611
is a set of assignable users UiThe user type is ctIs selected. In the step of the method,
U1＝{u8,u5,u4,u1}，
U2＝{u6,u2,u9,u7,u1}。
3) selecting a task to be assigned tau according to the following formulapTo be assigned task τpMoving into task set of allocated users'
Figure BDA00024018292000001612
|UiI is an allocable user set UiThe number of the users in (1) is,
Figure BDA00024018292000001613
is a set of assignable users UiThe user type is ctUser set of
Figure BDA00024018292000001614
If there is no task T to be allocated satisfying the conditionpThen the allocation ends. In the step, the number of tasks which meet the conditions and are to be distributed is 2, and the tasks to be distributed taupIs the task corresponding to the distributable user set with the minimum number of users, and the task to be distributed is tau1。
4) For each user type ct∈ C Press user ujExecution of task to be assigned τpYield v ofpjThe tasks to be allocated are respectively treated from large to smallpAssignable user set UpThe user type is ctUser set of
Figure BDA0002401829200000171
Is ranked by the user in (1), wherein
Figure BDA0002401829200000172
Figure BDA0002401829200000173
Is UpOf type CtIn this step, p is 1,
Figure BDA0002401829200000174
Figure BDA0002401829200000175
Figure BDA0002401829200000176
5) for each user type ct∈ C, respectively, from the task to be assigned τ according to the following formulapAssignable user set UpThe user type is ctUser set of
Figure BDA0002401829200000177
In, before selection
Figure BDA0002401829200000178
A user joins a task to be allocated taupSelected user set Up’
Figure BDA0002401829200000179
Figure BDA00024018292000001710
Figure BDA00024018292000001711
Wherein N ispIs the task τ to be assignedpThe number of persons required for the purpose of the treatment,
Figure BDA00024018292000001712
is the task τ to be assignedpSelected user set Up’The user type is ctIs selected. In the step of the method,
Figure BDA00024018292000001713
Figure BDA00024018292000001714
Figure BDA00024018292000001715
U1'＝{u8,u5,u4}。
6) per user ujExecution of task to be assigned τpYield v ofpjFrom large to small pairs of sets Up-Up’The users in (1) are ranked and the top N is selectedp-|Up’I users join selected user set Up’Wherein | Up’Is the selected user set Up’The number of users in (1). In this step, user u is selected1Joining the selected user set U1’，U1’Is { u8,u5,u4,u1}。
(4) Adjusting users assigned to selected tasks
1) Finding out the selected user's concentrated profitSmall user umin∈Up’And the user u with the maximum profit in the unselected user setmax∈Up-Up’If there is no user u with the minimum profitminAnd the user u with the maximum profitmaxSatisfies the following formula, step 5 of step (4) is carried out
vp,min＜vp,max
Wherein v isp,minAnd vp,maxRespectively the least profitable user uminAnd the user u with the maximum profitmaxExecution of task τpIn this step, there are no users u who satisfy the conditionminAnd user umaxGo to step 5).
5) Updating and task to be distributed tau according to the following formulapTask tau with time conflictsconf∈confCorresponding available user set Aconf
Aconf＝Aconf-Up'
Wherein
Figure BDA0002401829200000181
Wherein(s)conf,econf) Is related to task to be assigned τpTask tau with time conflictsconfUntil there is no task to be allocated which satisfies the condition, the execution time interval of (2) is transferred to step (3),conffor the empty set, after the task allocation is finished, the task allocation result is obtained as follows:
U1'＝{u8,u5,u4,u1}，
U2'＝{u6,u9,u1,u7,u2}。
and completing diversity-constrained crowd sensing task allocation.

Claims (5)

1. A diversity-constrained crowd-sourcing-aware task allocation method is characterized by comprising the following steps of:
(1) collecting user and perceptual tasks
Set of perceptual tasks ═ τ1，τ2，...，τnComposed of n tasks, where each task τiIs (N)i,si,ei) Corresponding to an execution time interval(s)i,ei) And a desired number of users Ni，siIs task τiStart time of (e)iIs task τiEnd time of (d) satisfies ei>siI ∈ {1, 2.·, n }, n being a finite positive integer, k being a finite set of user types C ═ C1,c2,…,ckThe number of the types of the users contained in the data;
user set U ═ U1,u2,…,umIs composed of m users, where each user ujCorresponding to a user type cj∈ C, an idle time interval(s)j,ej) And a set of executive tasks τiYield v ofijWherein, j ∈ {1, 2.. multidot.m }, e }j>sj，vij≥0；
Each task τiFor each user type ct∈ C has diversity constraint parameters
Figure FDA0002401829190000011
And
Figure FDA0002401829190000012
wherein, t ∈ {1, 2.., k },
Figure FDA0002401829190000013
(2) determining available users for a task
Determining per task τi∈ available user set AiThe following were used:
Ai＝{uj|uj∈U,sj≤si,ej≥ei}
(3) selecting task assignment users
1) For each task τi∈，Each user type ct∈ C, by user ujRevenue v for executing tasksijThe available user sets A are respectively set from large to smalliThe user type is ctUser set of
Figure FDA0002401829190000014
Wherein:
Figure FDA0002401829190000015
2) for each task τi∈, each user type ct∈ C, respectively from the available user set A as followsiThe user type is ctUser set of
Figure FDA0002401829190000016
Before selection in
Figure FDA0002401829190000017
Task tau is formed by individual usersiAssignable user set Ui
Figure FDA0002401829190000018
Figure FDA0002401829190000021
Figure FDA0002401829190000022
Wherein
Figure FDA0002401829190000023
Is a set of available users AiThe user type is ctUser set of
Figure FDA0002401829190000024
The number of the users in (1) is,
Figure FDA0002401829190000025
is a set of assignable users UiThe user type is ctA set of users of (1);
3) selecting a task to be assigned tau according to the following formulapTo be assigned task τpMoving into task set of allocated users'
Figure FDA0002401829190000026
|UiI is an allocable user set UiThe number of the users in (1) is,
Figure FDA0002401829190000027
is a set of assignable users UiThe user type is ctUser set of
Figure FDA0002401829190000028
If there is no task T to be allocated satisfying the conditionpIf yes, the distribution is finished;
4) for each user type ct∈ C Press user ujExecution of task to be assigned τpYield v ofpjThe tasks to be allocated are respectively treated from large to smallpAssignable user set UpThe user type is ctUser set of
Figure FDA0002401829190000029
Is ranked by the user in (1), wherein
Figure FDA00024018291900000210
5) For each user type ct∈ C, respectively, from the task to be assigned τ according to the following formulapAssignable user set UpThe user type is ctUser set of
Figure FDA00024018291900000211
In, before selection
Figure FDA00024018291900000212
A user joins a task to be allocated taupSelected user set Up’
Figure FDA00024018291900000213
Figure FDA00024018291900000214
Figure FDA00024018291900000215
Wherein N ispIs the task τ to be assignedpThe number of persons required for the purpose of the treatment,
Figure FDA00024018291900000216
is the task τ to be assignedpSelected user set Up’The user type is ctA set of users of (1);
6) per user ujExecution of task to be assigned τpYield v ofpjFrom large to small pairs of sets Up-Up’The users in (1) are ranked and the top N is selectedp-|Up’I users join selected user set Up’Wherein | Up’Is the selected user set Up’The number of users in (1);
(4) adjusting users assigned to selected tasks
1) Finding out the user u with the minimum profit in the selected user setmin∈Up’And the user u with the maximum profit in the unselected user setmax∈Up-Up’If there is no user u with the minimum profitminAnd the user u with the maximum profitmaxSatisfies the following formula, step 5 of step (4) is carried out
vp,min＜vp,max
Wherein v isp,minAnd vp,maxRespectively the least profitable user uminAnd the user u with the maximum profitmaxExecution of task τpThe profit of (2);
2) selecting the demoble user u 'with the minimum profit'min
Figure FDA0002401829190000031
Wherein v ispjIs user ujExecution of task to be assigned τpThe yield of (a) to (b) is,
Figure FDA0002401829190000032
is the task τ to be assignedpFor user ujUser type cjThe inverse diversity constraint parameter of (2) is,
Figure FDA0002401829190000033
is the selected user set Up’The user type is cjUser set of
Figure FDA0002401829190000034
The number of users in (1); if no go-to user u 'with minimum income exists'minTurning to step 5 of step (4);
3) selecting addable user u 'with maximum income'max
Figure FDA0002401829190000035
Wherein,
Figure FDA0002401829190000036
is the task τ to be assignedpFor user ujUser type cjForward diversity constraint parameters; if there is no maximum profitPlus user u'maxTurning to step 5 of step (4);
4) adding user u 'with maximum income'maxMove into Up’Go-to user u 'with minimal revenue'minMove out Up’
Up'＝Up'∪{u'max}-{u'min}
Step 1) of going to step (4);
5) updating and task to be distributed tau according to the following formulapTask tau with time conflictsconf∈confCorresponding available user set Aconf
Aconf＝Aconf-Up'
Wherein
Figure FDA0002401829190000041
Wherein(s)conf,econf) Is related to task to be assigned τpTask tau with time conflictsconfAnd (4) turning to the step (3) until no task to be allocated meeting the condition exists.
2. The diversity-constrained crowd-sensing task allocation method according to claim 1, wherein: in the step (1) of collecting users and perception tasks, the number N of the required users of the perception task setiIs any positive integer, NiIs integral multiple of k, and k is the number of types contained in the limited user type set C.
3. The diversity-constrained crowd-sensing task allocation method according to claim 1 or 2, wherein: in the step (1) of collecting users and sensing tasks, the number k of the user types contained in the limited user type set C is 3.
4. The diversity-constrained crowd-sensing task allocation method according to claim 1, wherein: in the step of collecting users and perceiving tasksIn step (1), the diversity constraint parameter
Figure FDA0002401829190000042
Figure FDA0002401829190000043
5. The diversity-constrained crowd-sensing task allocation method according to claim 1, wherein: in the step 3) of selecting the task allocation user, the task to be allocated taupComprises the following steps: the number of tasks satisfying the condition is at least 1, and the task t to be distributedpThe task corresponding to the assignable user set with the minimum number of users.
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